Modeling and targeting tumor-immune signaling interactions in tumor microenvironment

肿瘤微环境中肿瘤免疫信号相互作用的建模和靶向

• 批准号: 10659993
• 负责人: Fuhai Li
• 金额: $ 35.06万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-16 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659993
• 关键词: 3-Dimensional; Artificial Intelligence; Atlases; Biological Assay; CRISPR library; Cells; Chemoresistance; Coculture Techniques; Communication; Communities; Complex; Data; Data Set; Development; Diagnosis; Disease; Drug Combinations; Feedback; Fibroblasts; Genetic; Genetic Transcription; Heterogeneity; Human; Immune; Immune signaling; Immunotherapy; In Vitro; Individual; Knock-out; Knowledge; Malignant Neoplasms; Measurable; Measures; Modeling; Molecular Target; Mus; Neoplasm Metastasis; Pancreatic Ductal Adenocarcinoma; Patients; Pharmaceutical Preparations; Play; Process; Proliferating; Research; Resources; Role; Signal Pathway; Signal Transduction; Systems Biology; Technology; Time; Training; Tumor Tissue; Tumor-Associated Process; Tumor-associated macrophages; Validation; angiogenesis; cancer genetics; cell type; effective therapy; improved; in vivo; migration; mouse model; neoplastic cell; new therapeutic target; novel; pancreatic ductal adenocarcinoma model; response; restraint; single-cell RNA sequencing; success; targeted treatment; tool; treatment response; tumor; tumor growth; tumor microenvironment; tumor progression

Project Summary Tumor-stroma/immune cell signaling communications within the tumor microenvironment (TME) play important roles in tumor development and responses to targeted and immunotherapies. However, our knowledge of complex signaling communications within TME, and their roles in tumor development, drug and immunotherapy response is limited. Effective molecular targets are still missing that can inhibit the tumor- stroma signaling communications. Single cell RNA sequencing (scRNA-seq) has been being a powerful technology to capture transcriptional changes in individual tumor, stroma, immune cells within TME. While scRNA-seq datasets of human cancer are rapidly growing in number, which is leading to many basic and translational discoveries, the study of dynamic tumor-stroma signaling communications is limited. Limiting factors include: 1) static and single time-point snapshots of the complex interactions within the TME, and 2) difficulty in perturbing a large number of related signaling targets; and measuring corresponding functional effects to these perturbations in mouse or tumor tissues (to identify novel therapeutic targets and treatments). To resolve these challenges, in this study, we propose to combine the cutting-edge technologies, including novel artificial intelligence (AI) models, scRNA-seq, crispr-based single or double knockouts (CDKOs), 3D tumor-CAF-TAM co-culture assays, and genetic mouse models, in a systems biology manner. Specifically, (in Aim 1), we will develop novel network AI models using valuable large sets of scRNA-seq data of PDAC human tumors at WashU to identify static core tumor-CAF-TAM interaction (TCTi) signaling networks (multi-cell intra- /inter-cellular signaling networks of TCTi); and an initial set of anti-TCTi targets. In Aim 2, we will further develop another network AI model (M-Step) to infer the better anti-TCTi targets using the functional validation feedbacks in Aim 3; and predict synergistic drug combinations (inhibiting multiple key anti-TCTi targets). In Aim 3, the predicted targets and drugs will be efficiently evaluated using scalable 3D Tumor-CAF-TAM co-culture assays and crispr-based knockouts (E-step) with 3 measurable metrics, i.e., tumor proliferation, migration, angiogenesis. The M-step (modeling) and E-step (validation) forms an E-M process to identify key anti- TCTi targets and drugs iteratively. We will apply these AI models in Pancreatic ductal adenocarcinoma (PDAC) because 1) there have been very limited responses to immunotherapy; 2) no effective treatment; 3) nearly all patients will develop chemo-resistant and metastatic tumors within 2 years of diagnosis. Also (feasibility), 4) we have a strong cross-disciplinary team studying the PDAC TME (supported by NCI SPORE and human tumor atlas network (HTAN)), with the valuable state-of-the-art resources. The success of this study will identify novel anti-tumor-TAM-CAF targets and drug cocktails for PDAC treatment. The AI models, supporting the novel E-M systems biology, can be applied to other cancers and diseases.

项目概要 肿瘤微环境 (TME) 内的肿瘤基质/免疫细胞信号传导至关重要 在肿瘤发展以及对靶向和免疫疗法的反应中的作用。然而，我们的知识 TME 内复杂的信号通讯及其在肿瘤发展、药物和治疗中的作用 免疫治疗反应有限。仍然缺乏能够抑制肿瘤的有效分子靶点 基质信号传导通讯。单细胞 RNA 测序 (scRNA-seq) 一直是一种强大的 捕获 TME 内单个肿瘤、基质、免疫细胞转录变化的技术。尽管 人类癌症的 scRNA-seq 数据集的数量正在快速增长，这导致了许多基础和 由于转化发现，动态肿瘤间质信号传导通讯的研究是有限的。限制 因素包括：1) TME 内复杂交互的静态和单时间点快照，以及 2) 难以干扰大量相关信号目标；并测量相应的功能 这些扰动对小鼠或肿瘤组织的影响（以确定新的治疗靶点和治疗方法）。 为了解决这些挑战，在本研究中，我们建议结合尖端技术，包括 新颖的人工智能 (AI) 模型、scRNA-seq、基于 crispr 的单敲除或双敲除 (CDKO)、3D 以系统生物学方式进行肿瘤-CAF-TAM 共培养测定和遗传小鼠模型。具体来说，（在 目标 1)，我们将使用 PDAC 人类宝贵的大量 scRNA-seq 数据开发新颖的网络 AI 模型 华盛顿大学的肿瘤识别静态核心肿瘤-CAF-TAM 相互作用 (TCTi) 信号网络（多细胞内 /TCTi 的细胞间信号网络）；以及一组初始的反 TCTi 目标。在目标2中，我们将进一步 开发另一个网络 AI 模型 (M-Step)，以使用功能验证推断更好的抗 TCTi 目标 目标 3 中的反馈；并预测协同药物组合（抑制多个关键抗 TCTi 靶点）。瞄准 3、预测的靶点和药物将使用可扩展的3D Tumor-CAF-TAM共培养进行有效评估 检测和基于 crispr 的敲除（E-step），具有 3 个可测量指标，即肿瘤增殖、迁移、 血管生成。 M-step（建模）和E-step（验证）形成一个E-M过程来识别关键的反 TCTi 迭代靶向和药物。我们将把这些AI模型应用于胰腺导管腺癌 （PDAC）因为 1）对免疫疗法的反应非常有限； 2）无有效治疗； 3） 几乎所有患者在诊断后两年内都会出现化疗耐药和转移性肿瘤。还 （可行性），4）我们拥有一支强大的跨学科团队研究 PDAC TME（由 NCI SPORE 支持） 和人类肿瘤图谱网络（HTAN）），拥有宝贵的最先进的资源。此次活动的成功 研究将确定新的抗肿瘤 TAM-CAF 靶点和用于 PDAC 治疗的药物混合物。人工智能模型， 支持新颖的 E-M 系统生物学，可应用于其他癌症和疾病。